A detection algorithm for optical remote sensing targets was proposed based on the fused features contrast of subwindows. Firstly, a large number of varisized sliding windows were generated in a training image, and four types of scores related to multi-scale saliency, affine invariant region contrast, edge density and superpixel straddling were computed within each window. The feature parameters were learned on validation sets by maximizing localization accuracy and posterior probability. Then, all the features were combined in a Naive Bayesian framework and a classifier was trained. In the target detection step, the multi-scale saliency score was firstly computed within all the windows of test images, and partial windows with higher saliency and proper sizes matching to the objects to be detected were selected preliminarily. Furthermore, other scores were computed within the selected windows, and the posterior probability of each window was computed by using the trained classifier. Finally, windows with high local scores were selected and merged and the final detection results were obtained. The detection experiments were performed on three types of remote targets including planes, oilcans and ships, and the results show that each type of feature appears different properties for targets described, the highest accuracy is 74.21% to 80.32%. The proposed method outperforms all the single feature methods and the accuracy is improved to 80.87% to 87.30%. By compared with the fixed number sliding window algorithm, the accuracy rate is improved from about 80% to 85% and the false alarm rate is reduced from about 20% to 3%. Furthermore, the proposed method shows a 90% reduction in the number of windows and 25% reduction in the detection time due to the selection in the intermediary stage. It concludes that the method improves detection accuracy and algorithm efficiency greatly. © 2016, Science Press. All right reserved.
